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Measuring Market Risk - Reserve Bank of India

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MEASURING MARKET RISK 25<br />

Table 3: Number (Percentage) <strong>of</strong> VaR Violation*<br />

VaR Technique<br />

Security<br />

8.07% GS 2017 7.37% GS 2014<br />

Normal – Benchmark Model 15 (3.36) 14 (3.14)<br />

Historical Simulation - Simple 10 (2.24) 9 (2.02)<br />

Historical Simulation – Hybrid/Weighted<br />

λ = 0.94 9 (2.02) 9 (2.02)<br />

λ = 0.96 11 (2.47) 12 (2.69)<br />

λ= 0.98 12 (2.69) 15 (3.36)<br />

<strong>Risk</strong> Metric<br />

λ = 0.94 14 (3.14) 16 (3.59)<br />

λ = 0.96 12 (2.69) 16 (3.59)<br />

λ = 0.98 15 (3.36) 16 (3.59)<br />

Hyperbolic Distribution 7 (1.57) 6 (1.35)<br />

Tail Index 5 (1.12) 3 (0.67)<br />

Note: ‘*’ Figures inside ( ) are percentage <strong>of</strong> VaR-Violation. For a good VaR model this figure<br />

should be ideally equal to 1%.<br />

the theoretical 1% percentage value. This higher than expected<br />

frequency <strong>of</strong> VaR-violation is attributable to the underestimation<br />

<strong>of</strong> VaR numbers. The <strong>Risk</strong>Metric and hybrid historical simulation<br />

approaches also could not reduce this estimation bias and at times,<br />

the frequency <strong>of</strong> VaR-violation for <strong>Risk</strong>Metric even exceeds that <strong>of</strong><br />

the benchmark model. On the other hand, the accuracy level <strong>of</strong> VaR<br />

estimates obtained from ‘hyperbolic distribution’ and ‘tail-index’<br />

methods are much better. In fact, going by the closeness <strong>of</strong> observed<br />

frequency <strong>of</strong> VaR violation with the theoretical 1% level, the ‘tailindex’<br />

method appears to be producing most accurate VaR numbers<br />

followed by the method using ‘hyperbolic distribution’.<br />

In order to see whether the frequency <strong>of</strong> VaR-violation<br />

associated with competing VaR models can be considered as equal<br />

to the theoretical 1% value, we employed the popular Kupiec’s test.<br />

Relevant empirical results are presented in Table 4. As can be seen<br />

from this Table, the hypothesis that the frequency <strong>of</strong> VaR-violation is<br />

equal to the theoretical 1% value could not be accepted at 1% level<br />

<strong>of</strong> significance for the benchmark ‘normal’ method. The results show<br />

that the observed frequency is significantly higher than 1%, which<br />

indicates that the ‘normal’ method underestimates the VaR number.<br />

The <strong>Risk</strong> Metric approach also could not provide any improvement -

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